Application of Chaotic Neural Model Based on Olfactory System on Pattern Recognitions

This paper presents a simulation of a biological olfactory neural system with a KIII set, which is a high-dimensional chaotic neural network. The KIII set differs from conventional artificial neural networks by use of chaotic attractors for memory locations that are accessed by, chaotic trajectories. It was designed to simulate the patterns of action potentials and EEG waveforms observed in electrophysioloical experiments, and has proved its utility as a model for biological intelligence in pattern classification. An application on recognition of handwritten numerals is presented here, in which the classification performance of the KIII network under different noise levels was investigated.